@inproceedings{05ffca09bb4447ae8265f786ad986c33,
title = "Bias-Policy Iteration Based Adaptive Dynamic Programming for First-Order Fully Actuated Systems",
abstract = "In this paper, the bias-policy iteration (bias-PI) based adaptive dynamic programming for first-order fully actuated systems is considered. By exploiting the fully actuated property, the bias-PI method for first-order fully actuated systems is revisited, with a more streamlined formulation and a more concise convergence proof provided. The data-driven implementation for the proposed algorithm is introduced by neural networks accordingly. A numerical example verifies the effectiveness of the proposed results.",
keywords = "Admissible control, Data driven control, Fully actuated systems, Policy iteration, Reinforcement learning",
author = "Huaiyuan Jiang and Ruiqing Zhang and Bin Zhou",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 4th Conference on Fully Actuated System Theory and Applications, FASTA 2025 ; Conference date: 04-07-2025 Through 06-07-2025",
year = "2025",
doi = "10.1109/FASTA65681.2025.11138553",
language = "英语",
series = "Proceedings of the 4th Conference on Fully Actuated System Theory and Applications, FASTA 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2109--2114",
booktitle = "Proceedings of the 4th Conference on Fully Actuated System Theory and Applications, FASTA 2025",
address = "美国",
}